# Installing necessary packages
!pip install seaborn kaleido plotly matplotlib scikit-learn

# Importing libraries
import pandas as pd
import numpy as np
import seaborn as sns
import zipfile
import matplotlib.pyplot as plt
import kaleido
import plotly
import plotly.express as px
import json
from IPython.display import Image
from urllib.request import urlopen
from sklearn import datasets, linear_model

# Displaying versions to ensure correct installation
print("Kaleido version:", kaleido.__version__)
print("Plotly version:", plotly.__version__)

Requirement already satisfied: seaborn in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (0.13.2)
Requirement already satisfied: kaleido in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (0.2.1)
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Requirement already satisfied: matplotlib in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (3.9.0)
Collecting scikit-learn
  Downloading scikit_learn-1.5.0-cp312-cp312-macosx_12_0_arm64.whl.metadata (11 kB)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (from seaborn) (1.26.4)
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Requirement already satisfied: pyparsing>=2.3.1 in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (from matplotlib) (3.1.2)
Requirement already satisfied: python-dateutil>=2.7 in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)
Collecting scipy>=1.6.0 (from scikit-learn)
  Downloading scipy-1.13.1-cp312-cp312-macosx_12_0_arm64.whl.metadata (60 kB)
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  Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)
Collecting threadpoolctl>=3.1.0 (from scikit-learn)
  Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)
Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)
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Downloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)
Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn
Successfully installed joblib-1.4.2 scikit-learn-1.5.0 scipy-1.13.1 threadpoolctl-3.5.0
Kaleido version: 0.2.1
Plotly version: 5.22.0

zip_file_path = r'/Users/jetzeeveleens/Downloads/crime.zip'

with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    file_list = zip_ref.namelist()
    print(file_list)
    csv_file_name = 'crime_data_w_population_and_crime_rate.csv'
    zip_ref.extract(csv_file_name, '/tmp')

csv_file_path = '/tmp/' + csv_file_name
crime = pd.read_csv(csv_file_path)

display(crime)

['crime_data_w_population_and_crime_rate.csv']

zip_file_path = r'/Users/jetzeeveleens/Downloads/census.zip'
csv_file_name = 'acs2017_county_data.csv'

with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extract(csv_file_name, '/tmp')

csv_file_path = '/tmp/' + csv_file_name
census = pd.read_csv(csv_file_path)

census = census.drop(census[census["State"] == "Puerto Rico"].index)
display(census)

with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

nee = crime['county_name'].unique()
print(len(nee))

ja = census['County'].unique()
print(len(ja))

#Er zijn 3.142 counties in de Verenigde Staten

3136
1877

def convertToFipsForCensus(row):
    countyId = row["CountyId"]

    if countyId >= 10000:
        return str(countyId)

    return "0" + str(countyId)

census["fips"] = census.apply(lambda row: convertToFipsForCensus(row), axis = 1)
census.head()

def createFipsForCrime(row):
    cityFips = str(row["FIPS_CTY"])
    stateFips = str(row["FIPS_ST"])

    if len(cityFips) == 1:
        cityFips = "00" + cityFips

    if len(cityFips) == 2:
        cityFips = "0" + cityFips

    if len(stateFips) == 1:
        stateFips = "0" + stateFips

    return stateFips + cityFips

crime["fips"] = crime.apply(lambda row: createFipsForCrime(row), axis = 1)
crime.head()

crime_census = census.merge(crime, how="left", on="fips")

display(crime_census.head())
display(crime_census.shape)
print('----------------------------------------')
display(crime_census.isnull().sum())
print('--------------------------------------------------------------------------------')
print(crime_census.columns)

(3142, 62)

----------------------------------------

CountyId      0
State         0
County        0
TotalPop      0
Men           0
             ..
MVTHEFT       9
ARSON         9
population    9
FIPS_ST       9
FIPS_CTY      9
Length: 62, dtype: int64

--------------------------------------------------------------------------------
Index(['CountyId', 'State', 'County', 'TotalPop', 'Men', 'Women', 'Hispanic',
       'White', 'Black', 'Native', 'Asian', 'Pacific', 'VotingAgeCitizen',
       'Income', 'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty',
       'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',
       'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp',
       'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',
       'SelfEmployed', 'FamilyWork', 'Unemployment', 'fips', 'county_name',
       'crime_rate_per_100000', 'index', 'EDITION', 'PART', 'IDNO', 'CPOPARST',
       'CPOPCRIM', 'AG_ARRST', 'AG_OFF', 'COVIND', 'INDEX', 'MODINDX',
       'MURDER', 'RAPE', 'ROBBERY', 'AGASSLT', 'BURGLRY', 'LARCENY', 'MVTHEFT',
       'ARSON', 'population', 'FIPS_ST', 'FIPS_CTY'],
      dtype='object')

def showMap(df: pd.DataFrame, counties, target: str, colorscheme=px.colors.diverging.PRGn, min=5, max=50):
    fig = px.choropleth(df, geojson=counties, locations='fips', color=target,
                        range_color=(min, max),
                        scope="usa",
                        labels={target: f'{target}'}, 
                        color_continuous_midpoint=((max - min) / 3) + min, 
                        color_continuous_scale=colorscheme
                        )
    fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})

    return fig

minUnemployment = crime_census["crime_rate_per_100000"].min()
maxUnemployment = crime_census["crime_rate_per_100000"].max()
print(minUnemployment, maxUnemployment)

0.0 1791.995377

crime_census['black_hispanic'] = crime_census['Black'] + crime_census['Hispanic']

crimes_per = showMap(crime_census, counties, "crime_rate_per_100000", min=0, max=400, colorscheme='Reds')
crimes_per.show()

hispan = showMap(crime_census, counties, "VotingAgeCitizen", min=0, max=70000, colorscheme='emrld')
hispan.show()

print(crime_census['VotingAgeCitizen'].max(), crime_census['VotingAgeCitizen'].min())

6218279 59

total_pop = showMap(crime_census, counties, "TotalPop", min=0, max=75000, colorscheme='Greens')
total_pop.show()

print(crime_census['TotalPop'].max(), crime_census['TotalPop'].min())

10105722 74

income_cap = showMap(crime_census, counties, "Unemployment", min=0, max=10, colorscheme='greys')
income_cap.show()

print(crime_census['Unemployment'].max(), crime_census['Unemployment'].min())

28.8 0.0

child_pov = showMap(crime_census, counties, "Poverty", min=0, max=25, colorscheme='sunset')
child_pov.show()

print(crime_census['Poverty'].max(), crime_census['Poverty'].min())

52.0 2.4

income_cap = showMap(crime_census, counties, "IncomePerCap", min=9000, max=25000, colorscheme='mint')
income_cap.show()

print(crime_census['IncomePerCap'].max(), crime_census['IncomePerCap'].min())

69529 9334

# Verwijder rijen met ontbrekende waarden in de kolom 'crime_rate_per_100000'
crime_census_cleaned = crime_census.dropna(subset=['crime_rate_per_100000', 'ChildPoverty'])

from sklearn.linear_model import LinearRegression 
plt.figure(figsize=(10, 6))
sns.scatterplot(data=crime_census_cleaned, x='ChildPoverty', y='crime_rate_per_100000', marker='o', color='b')

plt.title('Correlatie tussen Crime Rate per 100,000 en Unemployment')
plt.xlabel('Kinderarmoede')
plt.ylabel('Criminaliteitscijfer per 100.000')

x = crime_census_cleaned[['ChildPoverty']]
y = crime_census_cleaned['crime_rate_per_100000']

reg = LinearRegression()
reg.fit(x, y)
predictions = reg.predict(x)

plt.plot(x, predictions, color='r')

plt.show()

correlation = crime_census['Income'].corr(crime_census['crime_rate_per_100000'], method='pearson')
print("Correlatie tussen het inkomen en de criminaliteits tarief:", round(correlation,2))

Correlatie tussen het inkomen en de criminaliteits tarief: -0.14

# Download the COVID-19 dataset
covid_url = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
covid_df = pd.read_csv(covid_url)

# Convert the date column to datetime format
covid_df['date'] = pd.to_datetime(covid_df['date'])

# Filter data for the year 2023
covid_df_2023 = covid_df[covid_df['date'].dt.year == 2023]

# Sort by date to ensure the latest data is selected for each country
covid_df_sorted = covid_df_2023.sort_values('date')

# Group by country and get the last entry for each country
covid_df_aggregated = covid_df_sorted.groupby('location').last().reset_index()

# Select relevant columns
covid_selected_columns = ['location', 'total_cases', 'total_deaths', 'total_vaccinations', 'population']
covid_df_aggregated = covid_df_aggregated[covid_selected_columns]

# Rename columns for clarity
covid_df_aggregated.rename(columns={'location': 'country'}, inplace=True)

# Display the aggregated DataFrame
display(covid_df_aggregated)

df2 = pd.read_csv('world_population.csv')
display(df2.head(), df2.isnull().sum())

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[29], line 1
----> 1 df2 = pd.read_csv('world_population.csv')
      2 display(df2.head(), df2.isnull().sum())

File /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
   1013 kwds_defaults = _refine_defaults_read(
   1014     dialect,
   1015     delimiter,
   (...)
   1022     dtype_backend=dtype_backend,
   1023 )
   1024 kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
    617 _validate_names(kwds.get("names", None))
    619 # Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
    622 if chunksize or iterator:
    623     return parser

File /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   1617     self.options["has_index_names"] = kwds["has_index_names"]
   1619 self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
   1878     if "b" not in mode:
   1879         mode += "b"
-> 1880 self.handles = get_handle(
   1881     f,
   1882     mode,
   1883     encoding=self.options.get("encoding", None),
   1884     compression=self.options.get("compression", None),
   1885     memory_map=self.options.get("memory_map", False),
   1886     is_text=is_text,
   1887     errors=self.options.get("encoding_errors", "strict"),
   1888     storage_options=self.options.get("storage_options", None),
   1889 )
   1890 assert self.handles is not None
   1891 f = self.handles.handle

File /opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.12/site-packages/pandas/io/common.py:873, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    868 elif isinstance(handle, str):
    869     # Check whether the filename is to be opened in binary mode.
    870     # Binary mode does not support 'encoding' and 'newline'.
    871     if ioargs.encoding and "b" not in ioargs.mode:
    872         # Encoding
--> 873         handle = open(
    874             handle,
    875             ioargs.mode,
    876             encoding=ioargs.encoding,
    877             errors=errors,
    878             newline="",
    879         )
    880     else:
    881         # Binary mode
    882         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'world_population.csv'

df3 = covid_df_aggregated.merge(df2, left_on='country', right_on='Country/Territory', how='inner')
display(df3.head())

correlation = df3['total_cases'].corr(df3['Density (per km²)'], method='pearson')
print("Pearson's correlation coefficient:", round(correlation,2))

correlation = df3['total_cases'].corr(df3['Area (km²)'], method='pearson')
print("Pearson's correlation coefficient:", round(correlation,2))

correlation = df3['total_cases'].corr(df3['population'], method='pearson')
print("Pearson's correlation coefficient:", round(correlation,2))

	county_name	crime_rate_per_100000	index	EDITION	PART	IDNO	CPOPARST	CPOPCRIM	AG_ARRST	AG_OFF	...	RAPE	ROBBERY	AGASSLT	BURGLRY	LARCENY	MVTHEFT	ARSON	population	FIPS_ST	FIPS_CTY
0	St. Louis city, MO	1791.995377	1	1	4	1612	318667	318667	15	15	...	200	1778	3609	4995	13791	3543	464	318416	29	510
1	Crittenden County, AR	1754.914968	2	1	4	130	50717	50717	4	4	...	38	165	662	1482	1753	189	28	49746	5	35
2	Alexander County, IL	1664.700485	3	1	4	604	8040	8040	2	2	...	2	5	119	82	184	12	2	7629	17	3
3	Kenedy County, TX	1456.310680	4	1	4	2681	444	444	1	1	...	3	1	2	5	4	4	0	412	48	261
4	De Soto Parish, LA	1447.402430	5	1	4	1137	26971	26971	3	3	...	4	17	368	149	494	60	0	27083	22	31
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3131	Ohio County, IN	0.000000	3132	1	4	762	6084	0	2	1	...	0	0	0	2	2	0	0	5994	18	115
3132	Newton County, MS	0.000000	3133	1	4	1465	21545	3346	3	1	...	0	0	0	4	0	1	0	21689	28	101
3133	Jerauld County, SD	0.000000	3134	1	4	2424	2108	2108	1	1	...	0	0	0	1	3	1	0	2066	46	73
3134	Cimarron County, OK	0.000000	3135	1	4	2167	2502	2502	2	2	...	0	0	0	1	2	0	0	2335	40	25
3135	Lawrence County, MS	0.000000	3136	1	4	1453	12714	0	1	1	...	0	0	0	0	0	0	0	12514	28	77

	CountyId	State	County	TotalPop	Men	Women	Hispanic	White	Black	Native	...	Walk	OtherTransp	WorkAtHome	MeanCommute	Employed	PrivateWork	PublicWork	SelfEmployed	FamilyWork	Unemployment
0	1001	Alabama	Autauga County	55036	26899	28137	2.7	75.4	18.9	0.3	...	0.6	1.3	2.5	25.8	24112	74.1	20.2	5.6	0.1	5.2
1	1003	Alabama	Baldwin County	203360	99527	103833	4.4	83.1	9.5	0.8	...	0.8	1.1	5.6	27.0	89527	80.7	12.9	6.3	0.1	5.5
2	1005	Alabama	Barbour County	26201	13976	12225	4.2	45.7	47.8	0.2	...	2.2	1.7	1.3	23.4	8878	74.1	19.1	6.5	0.3	12.4
3	1007	Alabama	Bibb County	22580	12251	10329	2.4	74.6	22.0	0.4	...	0.3	1.7	1.5	30.0	8171	76.0	17.4	6.3	0.3	8.2
4	1009	Alabama	Blount County	57667	28490	29177	9.0	87.4	1.5	0.3	...	0.4	0.4	2.1	35.0	21380	83.9	11.9	4.0	0.1	4.9
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3137	56037	Wyoming	Sweetwater County	44527	22981	21546	16.0	79.6	0.8	0.6	...	2.8	1.3	1.5	20.5	22739	78.4	17.8	3.8	0.0	5.2
3138	56039	Wyoming	Teton County	22923	12169	10754	15.0	81.5	0.5	0.3	...	11.7	3.8	5.7	14.3	14492	82.1	11.4	6.5	0.0	1.3
3139	56041	Wyoming	Uinta County	20758	10593	10165	9.1	87.7	0.1	0.9	...	1.1	1.3	2.0	19.9	9528	71.5	21.5	6.6	0.4	6.4
3140	56043	Wyoming	Washakie County	8253	4118	4135	14.2	82.2	0.3	0.4	...	6.9	1.3	4.4	14.3	3833	69.8	22.0	8.1	0.2	6.1
3141	56045	Wyoming	Weston County	7117	3756	3361	1.4	91.6	0.5	0.1	...	3.0	1.6	6.9	25.7	3407	68.2	21.9	8.8	1.1	2.2

	CountyId	State	County	TotalPop	Men	Women	Hispanic	White	Black	Native	...	OtherTransp	WorkAtHome	MeanCommute	Employed	PrivateWork	PublicWork	SelfEmployed	FamilyWork	Unemployment	fips
0	1001	Alabama	Autauga County	55036	26899	28137	2.7	75.4	18.9	0.3	...	1.3	2.5	25.8	24112	74.1	20.2	5.6	0.1	5.2	01001
1	1003	Alabama	Baldwin County	203360	99527	103833	4.4	83.1	9.5	0.8	...	1.1	5.6	27.0	89527	80.7	12.9	6.3	0.1	5.5	01003
2	1005	Alabama	Barbour County	26201	13976	12225	4.2	45.7	47.8	0.2	...	1.7	1.3	23.4	8878	74.1	19.1	6.5	0.3	12.4	01005
3	1007	Alabama	Bibb County	22580	12251	10329	2.4	74.6	22.0	0.4	...	1.7	1.5	30.0	8171	76.0	17.4	6.3	0.3	8.2	01007
4	1009	Alabama	Blount County	57667	28490	29177	9.0	87.4	1.5	0.3	...	0.4	2.1	35.0	21380	83.9	11.9	4.0	0.1	4.9	01009

	county_name	crime_rate_per_100000	index	EDITION	PART	IDNO	CPOPARST	CPOPCRIM	AG_ARRST	AG_OFF	...	ROBBERY	AGASSLT	BURGLRY	LARCENY	MVTHEFT	ARSON	population	FIPS_ST	FIPS_CTY	fips
0	St. Louis city, MO	1791.995377	1	1	4	1612	318667	318667	15	15	...	1778	3609	4995	13791	3543	464	318416	29	510	29510
1	Crittenden County, AR	1754.914968	2	1	4	130	50717	50717	4	4	...	165	662	1482	1753	189	28	49746	5	35	05035
2	Alexander County, IL	1664.700485	3	1	4	604	8040	8040	2	2	...	5	119	82	184	12	2	7629	17	3	17003
3	Kenedy County, TX	1456.310680	4	1	4	2681	444	444	1	1	...	1	2	5	4	4	0	412	48	261	48261
4	De Soto Parish, LA	1447.402430	5	1	4	1137	26971	26971	3	3	...	17	368	149	494	60	0	27083	22	31	22031

	CountyId	State	County	TotalPop	Men	Women	Hispanic	White	Black	Native	...	RAPE	ROBBERY	AGASSLT	BURGLRY	LARCENY	MVTHEFT	ARSON	population	FIPS_ST	FIPS_CTY
0	1001	Alabama	Autauga County	55036	26899	28137	2.7	75.4	18.9	0.3	...	15.0	34.0	87.0	447.0	1233.0	85.0	108.0	55246.0	1.0	1.0
1	1003	Alabama	Baldwin County	203360	99527	103833	4.4	83.1	9.5	0.8	...	30.0	76.0	332.0	967.0	3829.0	192.0	31.0	195540.0	1.0	3.0
2	1005	Alabama	Barbour County	26201	13976	12225	4.2	45.7	47.8	0.2	...	4.0	8.0	36.0	90.0	362.0	21.0	0.0	27076.0	1.0	5.0
3	1007	Alabama	Bibb County	22580	12251	10329	2.4	74.6	22.0	0.4	...	4.0	8.0	36.0	122.0	251.0	27.0	0.0	22512.0	1.0	7.0
4	1009	Alabama	Blount County	57667	28490	29177	9.0	87.4	1.5	0.3	...	11.0	9.0	101.0	397.0	865.0	86.0	9.0	57872.0	1.0	9.0

Datavisualisatie Draft

Part A

Part B

	country	total_cases	total_deaths	total_vaccinations	population
0	Afghanistan	230375.0	7973.0	2.296475e+07	4.112877e+07
1	Africa	13133432.0	259066.0	8.632379e+08	1.426737e+09
2	Albania	334596.0	3604.0	3.088966e+06	2.842318e+06
3	Algeria	272010.0	6881.0	NaN	4.490323e+07
4	American Samoa	8359.0	34.0	NaN	4.429500e+04
...	...	...	...	...	...
248	Wallis and Futuna	3550.0	8.0	1.805800e+04	1.159600e+04
249	World	773948532.0	7015947.0	1.357576e+10	7.975105e+09
250	Yemen	11945.0	2159.0	1.298654e+06	3.369661e+07
251	Zambia	349304.0	4069.0	1.345421e+07	2.001767e+07
252	Zimbabwe	266071.0	5731.0	NaN	1.632054e+07